import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				UnCLIPImageVariationPipeline
   __lowerCAmelCase           =				IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
   __lowerCAmelCase           =				IMAGE_VARIATION_BATCH_PARAMS

   __lowerCAmelCase           =				[
       """generator""",
       """return_dict""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]
   __lowerCAmelCase           =				False







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    return 32







   @property
   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    return 32







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return self.time_input_dim







   @property
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return self.time_input_dim * 4







   @property
   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    return 100







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)
    return tokenizer







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    torch.manual_seed(0				)
    UpperCamelCase			  =      CLIPTextConfig(
        bos_token_id=0   ,   eos_token_id=2   ,   hidden_size=self.text_embedder_hidden_size   ,   projection_dim=self.text_embedder_hidden_size   ,   intermediate_size=37   ,   layer_norm_eps=1E-05   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   )
    return CLIPTextModelWithProjection(lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    torch.manual_seed(0				)
    UpperCamelCase			  =      CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size   ,   projection_dim=self.text_embedder_hidden_size   ,   num_hidden_layers=5   ,   num_attention_heads=4   ,   image_size=32   ,   intermediate_size=37   ,   patch_size=1   ,   )
    return CLIPVisionModelWithProjection(lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    torch.manual_seed(0				)

    UpperCamelCase			  =      {
        """clip_embeddings_dim""": self.text_embedder_hidden_size,
        """time_embed_dim""": self.time_embed_dim,
        """cross_attention_dim""": self.cross_attention_dim,
    }

    UpperCamelCase			  =      UnCLIPTextProjModel(**lowerCamelCase_				)
    return model







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    torch.manual_seed(0				)

    UpperCamelCase			  =      {
        """sample_size""": 32,
        # RGB in channels
        """in_channels""": 3,
        # Out channels is double in channels because predicts mean and variance
        """out_channels""": 6,
        """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
        """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
        """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
        """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
        """layers_per_block""": 1,
        """cross_attention_dim""": self.cross_attention_dim,
        """attention_head_dim""": 4,
        """resnet_time_scale_shift""": """scale_shift""",
        """class_embed_type""": """identity""",
    }

    UpperCamelCase			  =      UNetaDConditionModel(**lowerCamelCase_				)
    return model







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    return {
        "sample_size": 64,
        "layers_per_block": 1,
        "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
        "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
        "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
        "in_channels": 6,
        "out_channels": 3,
    }







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    torch.manual_seed(0				)

    UpperCamelCase			  =      UNetaDModel(**self.dummy_super_res_kwargs				)
    return model







   @property
   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    torch.manual_seed(1				)

    UpperCamelCase			  =      UNetaDModel(**self.dummy_super_res_kwargs				)
    return model







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.dummy_decoder
    UpperCamelCase			  =      self.dummy_text_proj
    UpperCamelCase			  =      self.dummy_text_encoder
    UpperCamelCase			  =      self.dummy_tokenizer
    UpperCamelCase			  =      self.dummy_super_res_first
    UpperCamelCase			  =      self.dummy_super_res_last

    UpperCamelCase			  =      UnCLIPScheduler(
        variance_type="""learned_range"""   ,   prediction_type="""epsilon"""   ,   num_train_timesteps=1000   ,   )

    UpperCamelCase			  =      UnCLIPScheduler(
        variance_type="""fixed_small_log"""   ,   prediction_type="""epsilon"""   ,   num_train_timesteps=1000   ,   )

    UpperCamelCase			  =      CLIPImageProcessor(crop_size=32   ,   size=32				)

    UpperCamelCase			  =      self.dummy_image_encoder

    return {
        "decoder": decoder,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "text_proj": text_proj,
        "feature_extractor": feature_extractor,
        "image_encoder": image_encoder,
        "super_res_first": super_res_first,
        "super_res_last": super_res_last,
        "decoder_scheduler": decoder_scheduler,
        "super_res_scheduler": super_res_scheduler,
    }







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Union[str, Any]=0   ,   lowerCamelCase_						:						List[str]=True				):





    """simple docstring"""


    UpperCamelCase			  =      floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
    if str(lowerCamelCase_				).startswith("""mps"""				):
     UpperCamelCase			  =      torch.manual_seed(lowerCamelCase_				)
    else:
     UpperCamelCase			  =      torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)

    if pil_image:
     UpperCamelCase			  =      input_image * 0.5 + 0.5
     UpperCamelCase			  =      input_image.clamp(0   ,   1				)
     UpperCamelCase			  =      input_image.cpu().permute(0   ,   2   ,   3   ,   1				).float().numpy()
     UpperCamelCase			  =      DiffusionPipeline.numpy_to_pil(lowerCamelCase_				)[0]

    return {
        "image": input_image,
        "generator": generator,
        "decoder_num_inference_steps": 2,
        "super_res_num_inference_steps": 2,
        "output_type": "np",
    }







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      """cpu"""

    UpperCamelCase			  =      self.get_dummy_components()

    UpperCamelCase			  =      self.pipeline_class(**lowerCamelCase_				)
    UpperCamelCase			  =      pipe.to(lowerCamelCase_				)

    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)

    UpperCamelCase			  =      pipe(**lowerCamelCase_				)
    UpperCamelCase			  =      output.images

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)

    UpperCamelCase			  =      pipe(
        **lowerCamelCase_   ,   return_dict=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      image[0, -3:, -3:, -1]
    UpperCamelCase			  =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)

    UpperCamelCase			  =      np.array(
        [
            0.9_9_9_7,
            0.0_0_0_2,
            0.9_9_9_7,
            0.9_9_9_7,
            0.9_9_6_9,
            0.0_0_2_3,
            0.9_9_9_7,
            0.9_9_6_9,
            0.9_9_7_0,
        ]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      """cpu"""

    UpperCamelCase			  =      self.get_dummy_components()

    UpperCamelCase			  =      self.pipeline_class(**lowerCamelCase_				)
    UpperCamelCase			  =      pipe.to(lowerCamelCase_				)

    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)

    UpperCamelCase			  =      pipe(**lowerCamelCase_				)
    UpperCamelCase			  =      output.images

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)

    UpperCamelCase			  =      pipe(
        **lowerCamelCase_   ,   return_dict=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      image[0, -3:, -3:, -1]
    UpperCamelCase			  =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)

    UpperCamelCase			  =      np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      """cpu"""

    UpperCamelCase			  =      self.get_dummy_components()

    UpperCamelCase			  =      self.pipeline_class(**lowerCamelCase_				)
    UpperCamelCase			  =      pipe.to(lowerCamelCase_				)

    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)
    UpperCamelCase			  =      [
        pipeline_inputs["""image"""],
        pipeline_inputs["""image"""],
    ]

    UpperCamelCase			  =      pipe(**lowerCamelCase_				)
    UpperCamelCase			  =      output.images

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)
    UpperCamelCase			  =      [
        tuple_pipeline_inputs["""image"""],
        tuple_pipeline_inputs["""image"""],
    ]

    UpperCamelCase			  =      pipe(
        **lowerCamelCase_   ,   return_dict=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      image[0, -3:, -3:, -1]
    UpperCamelCase			  =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (2, 64, 64, 3)

    UpperCamelCase			  =      np.array(
        [
            0.9_9_9_7,
            0.9_9_8_9,
            0.0_0_0_8,
            0.0_0_2_1,
            0.9_9_6_0,
            0.0_0_1_8,
            0.0_0_1_4,
            0.0_0_0_2,
            0.9_9_3_3,
        ]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      torch.device("""cpu"""				)
    class SCREAMING_SNAKE_CASE_		:
       __lowerCAmelCase           =				1





    UpperCamelCase			  =      self.get_dummy_components()

    UpperCamelCase			  =      self.pipeline_class(**lowerCamelCase_				)
    UpperCamelCase			  =      pipe.to(lowerCamelCase_				)

    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      torch.Generator(device=lowerCamelCase_				).manual_seed(0				)
    UpperCamelCase			  =      pipe.decoder.dtype
    UpperCamelCase			  =      1

    UpperCamelCase			  =      (
        batch_size,
        pipe.decoder.config.in_channels,
        pipe.decoder.config.sample_size,
        pipe.decoder.config.sample_size,
    )
    UpperCamelCase			  =      pipe.prepare_latents(
        lowerCamelCase_   ,   dtype=lowerCamelCase_   ,   device=lowerCamelCase_   ,   generator=lowerCamelCase_   ,   latents=lowerCamelCase_   ,   scheduler=DummyScheduler()				)

    UpperCamelCase			  =      (
        batch_size,
        pipe.super_res_first.config.in_channels // 2,
        pipe.super_res_first.config.sample_size,
        pipe.super_res_first.config.sample_size,
    )
    UpperCamelCase			  =      pipe.prepare_latents(
        lowerCamelCase_   ,   dtype=lowerCamelCase_   ,   device=lowerCamelCase_   ,   generator=lowerCamelCase_   ,   latents=lowerCamelCase_   ,   scheduler=DummyScheduler()				)

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)

    UpperCamelCase			  =      pipe(
        **lowerCamelCase_   ,   decoder_latents=lowerCamelCase_   ,   super_res_latents=lowerCamelCase_				).images

    UpperCamelCase			  =      self.get_dummy_inputs(lowerCamelCase_   ,   pil_image=lowerCamelCase_				)
    # Don't pass image, instead pass embedding
    UpperCamelCase			  =      pipeline_inputs.pop("""image"""				)
    UpperCamelCase			  =      pipe.image_encoder(lowerCamelCase_				).image_embeds

    UpperCamelCase			  =      pipe(
        **lowerCamelCase_   ,   decoder_latents=lowerCamelCase_   ,   super_res_latents=lowerCamelCase_   ,   image_embeddings=lowerCamelCase_   ,   ).images

    # make sure passing text embeddings manually is identical
    assert np.abs(img_out_a - img_out_a				).max() < 1E-4







   @skip_mps
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      torch_device == """cpu"""

    # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
    UpperCamelCase			  =      1E-2

    self._test_attention_slicing_forward_pass(
        test_max_difference=lowerCamelCase_   ,   expected_max_diff=lowerCamelCase_				)







   @skip_mps
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      torch_device == """cpu"""
    UpperCamelCase			  =      True
    UpperCamelCase			  =      [
        """decoder_num_inference_steps""",
        """super_res_num_inference_steps""",
    ]

    self._test_inference_batch_single_identical(
        test_max_difference=lowerCamelCase_   ,   relax_max_difference=lowerCamelCase_   ,   additional_params_copy_to_batched_inputs=lowerCamelCase_   ,   )







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      [
        """decoder_num_inference_steps""",
        """super_res_num_inference_steps""",
    ]

    if torch_device == "mps":
     # TODO: MPS errors with larger batch sizes
     UpperCamelCase			  =      [2, 3]
     self._test_inference_batch_consistent(
         batch_sizes=lowerCamelCase_   ,   additional_params_copy_to_batched_inputs=lowerCamelCase_   ,   )
    else:
     self._test_inference_batch_consistent(
         additional_params_copy_to_batched_inputs=lowerCamelCase_				)







   @skip_mps
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return super().test_dict_tuple_outputs_equivalent()







   @skip_mps
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    return super().test_save_load_local()







   @skip_mps
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    return super().test_save_load_optional_components()





@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""				)
    UpperCamelCase			  =      load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""				)

    UpperCamelCase			  =      UnCLIPImageVariationPipeline.from_pretrained(
        """kakaobrain/karlo-v1-alpha-image-variations"""   ,   torch_dtype=torch.floataa				)
    UpperCamelCase			  =      pipeline.to(lowerCamelCase_				)
    pipeline.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      torch.Generator(device="""cpu"""				).manual_seed(0				)
    UpperCamelCase			  =      pipeline(
        lowerCamelCase_   ,   generator=lowerCamelCase_   ,   output_type="""np"""   ,   )

    UpperCamelCase			  =      output.images[0]

    assert image.shape == (256, 256, 3)

    assert_mean_pixel_difference(lowerCamelCase_   ,   lowerCamelCase_   ,   15				)

import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
